Synergistic Detrimental Effects of Cigarette Smoke, Alcohol, and SARS-CoV-2 in COPD Bronchial Epithelial Cells
Bibliographic record
Abstract
Lung conditions such as COPD, as well as risk factors such as alcohol misuse and cigarette smoking, can exacerbate COVID-19 disease severity. Synergistically, these risk factors can have a significant impact on immunity against pathogens. Here, we studied the effect of a short exposure to alcohol and/or cigarette smoke extract (CSE) in vitro on acute SARS-CoV-2 infection of ciliated human bronchial epithelial cells (HBECs) collected from healthy and COPD donors. We observed an increase in viral titer in CSE- or alcohol-treated COPD HBECs compared to untreated COPD HBECs. Furthermore, we treated healthy HBECs accompanied by enhanced lactate dehydrogenase activity, indicating exacerbated injury. Finally, IL-8 secretion was elevated due to the synergistic damage mediated by alcohol, CSE, and SARS-CoV-2 in COPD HBECs. Together, our data suggest that, with pre-existing COPD, short exposure to alcohol or CSE is sufficient to exacerbate SARS-CoV-2 infection and associated injury, impairing lung defences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".